Genetic algorithm-based regularization parameter estimation for the inverse electrocardiography problem using multiple constraints.
In inverse electrocardiography, the goal is to estimate cardiac electrical sources from potential measurements on the body surface. It is by nature an ill-posed problem, and regularization must be employed to obtain reliable solutions. This paper employs the multiple constraint solution approach pro...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 51; no. 4; pp. 367 - 376 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Apr2013
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104247026&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104247026 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Apr2013 vid: 51 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104247026 NLM23224834 2012034939 10.1007/s11517-012-1005-6 NLM23224834 104247026 ppf: 367 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Genetic algorithm-based regularization parameter estimation for the inverse electrocardiography problem using multiple constraints. aug: au: Serinagaoglu Dogrusoz, Yesim Mazloumi Gavgani, Alireza Dogrusoz, Yesim Serinagaoglu Gavgani, Alireza Mazloumi affil: Electrical and Electronics Engineering Department, Middle East Technical University, Ankara, Turkey sug: subj: Algorithms Electrocardiography Methods Models, Biological Body Surface Potential Mapping Computer Simulation Human Signal Processing, Computer Assisted Sensitivity and Specificity ab: In inverse electrocardiography, the goal is to estimate cardiac electrical sources from potential measurements on the body surface. It is by nature an ill-posed problem, and regularization must be employed to obtain reliable solutions. This paper employs the multiple constraint solution approach proposed in Brooks et al. (IEEE Trans Biomed Eng 46(1):3-18, 1999) and extends its practical applicability to include more than two constraints by finding appropriate values for the multiple regularization parameters. Here, we propose the use of real-valued genetic algorithms for the estimation of multiple regularization parameters. Theoretically, it is possible to include as many constraints as necessary and find the corresponding regularization parameters using this approach. We have shown the feasibility of our method using two and three constraints. The results indicate that GA could be a good approach for the estimation of multiple regularization parameters. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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